Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance

In this paper we present a linear regression model for modal symbolic data. The observed variables are histogram variables according to the definition given in the framework of Symbolic Data Analysis and the parameters of the model are estimated using the classic Least Squares method. An appropriate metric is introduced in order to measure the error between the observed and the predicted distributions. In particular, the Wasserstein distance is proposed. Some properties of such metric are exploited to predict the response variable as direct linear combination of other independent histogram variables. Measures of goodness of fit are discussed. An application on real data corroborates the proposed method

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Complementing visual data mining with the sound dimension: Sonification of time dependent data

This chapter explores the extension of visual data mining by adding a sound dimension to the data representation. It presents the results of an early 2001 experiments with sonification of 2D and 3D time series data. A number of sonification means for these experiments have been implemented. The goal of these experiments was to determine how sonification of two and three-dimensional graphs can support and complement or even be an alternative to visually displayed graphs. The research methodology used the triangulation method, combining the automated generation of the sound patterns with two evaluation techniques. The first one included the assessment and evaluation of the sound sequences of the sonified data by the participants in the experiment via a dedicated server. The second one was based on the analysis of an evaluation questionnaire, filled by each participant that performed the tests. The chapter presents the results and the issues raised by the experiments. © 2008 Springer-Verlag Berlin Heidelberg

A New Approach To Classical Backpropagation Algorithm For Neuro-Fuzzy-GA . . .

The aim goal of this paper is to present a new learning algorithm, which has been applied to feedforward neural networks, as well as complexity problems to optimize the topology of type of network. We used this approach not only during the learning phase of the network, but also to optimize the number of hidden neurons. This learning algorithm is inspired on the classical backpropagation algorithm. It owns some variations due to kind of network used. The algorithm developed was applied to a particular network which has AND/OR fuzzy neurons

Training Algorithm For Neuro-Fuzzy-GA Systems

The main goal of this paper is to present a new learning algorithm which has been applied to feedforward neural networks. It was used not only during the learning phase of the network, but also to optimise the number of hidden neurons. This learning algorithm is inspired on the classical backpropagation algorithm but it owns some variations due to kind of network used. This algorithm was applied to a particular network which has AND/OR fuzzy neurons

Automated evaluation of web usability and accessibility by guideline review

A novel approach is presented for automatically evaluating of the usability and accessibility (U&A) of web sites by performing a static analysis of their HTML code against U&A guidelines. The approach relies on separating guidelines evaluation logic from the evaluation engine. Due to this separation, the whole evaluation process can be divided into two main phases: specifying formal guidelines and web page evaluation. In the first phase, the formal structure of a guideline is expressed in terms of Guideline Definition Language (GDL). In the second phase, the web page is parsed to identify its contents and structure and link them to relevant guidelines to be evaluated on the page parsed. This approach enables the simultaneous evaluation of multiple guidelines selected on demand from different sources. It also optimises evaluation by automatically identifying common sub-structures among structured guidelines. It also supports the expression, by evaluators with different usability practises, of alternative evaluation strategies